Thermodynamics of Fragment Binding

The ligand binding pockets of proteins have preponderance of hydrophobic amino acids and are typically within the apolar interior of the protein; nevertheless, they are able to bind low complexity, polar, water-soluble fragments. In order to understand this phenomenon, we analyzed high resolution X-ray data of protein–ligand complexes from the Protein Data Bank and found that fragments bind to proteins with two near optimal geometry H-bonds on average. The linear extent of the fragment binding site was found not to be larger than 10 Å, and the H-bonding region was found to be restricted to about 5 Å on average. The number of conserved H-bonds in proteins cocrystallized with multiple different fragments is also near to 2. These fragment binding sites that are able to form limited number of strong H-bonds in a hydrophobic environment are identified as hot spots. An estimate of the free-energy gain of H-bond formation versus apolar desolvation supports that fragment sized compounds need H-bonds to achieve detectable binding. This suggests that fragment binding is mostly enthalpic that is in line with their observed binding thermodynamics documented in Isothermal Titration Calorimetry (ITC) data sets and gives a thermodynamic rationale for fragment based approaches. The binding of larger compounds tends to more rely on apolar desolvation with a corresponding increase of the entropy content of their binding free-energy. These findings explain the reported size-dependence of maximal available affinity and ligand efficiency both behaving differently in the small molecule region featured by strong H-bond formation and in the larger molecule region featured by apolar desolvation

Thermodynamics of Fragment Binding

The ligand binding pockets of proteins have preponderance of hydrophobic amino acids and are typically within the apolar interior of the protein; nevertheless, they are able to bind low complexity, polar, water-soluble fragments. In order to understand this phenomenon, we analyzed high resolution X-ray data of protein–ligand complexes from the Protein Data Bank and found that fragments bind to proteins with two near optimal geometry H-bonds on average. The linear extent of the fragment binding site was found not to be larger than 10 Å, and the H-bonding region was found to be restricted to about 5 Å on average. The number of conserved H-bonds in proteins cocrystallized with multiple different fragments is also near to 2. These fragment binding sites that are able to form limited number of strong H-bonds in a hydrophobic environment are identified as hot spots. An estimate of the free-energy gain of H-bond formation versus apolar desolvation supports that fragment sized compounds need H-bonds to achieve detectable binding. This suggests that fragment binding is mostly enthalpic that is in line with their observed binding thermodynamics documented in Isothermal Titration Calorimetry (ITC) data sets and gives a thermodynamic rationale for fragment based approaches. The binding of larger compounds tends to more rely on apolar desolvation with a corresponding increase of the entropy content of their binding free-energy. These findings explain the reported size-dependence of maximal available affinity and ligand efficiency both behaving differently in the small molecule region featured by strong H-bond formation and in the larger molecule region featured by apolar desolvation

How Are Fragments Optimized? A Retrospective Analysis of 145 Fragment Optimizations

Fragment optimizations in nearly 150 fragment-based drug discovery programs reported in the literature during the past fifteen years were investigated. By analyzing physicochemical properties and ligand efficiency indices we found that biochemical detection methods yield hits with superior ligand efficiency and lipophilicity indices than do X-ray and NMR. These advantageous properties are partially preserved in the optimization since higher affinity starting points allow optimizations better balanced between affinity and physicochemical property improvements. Size independent ligand efficiency (SILE) and lipophilic indices (primarily LELP) were found to be appropriate metrics to monitor optimizations. Small and medium enterprises (SME) produce optimized compounds with better properties than do big pharma companies and universities. It appears that the use of target structural information is a major reason behind this finding. Structure-based optimization was also found to dominate successful fragment optimizations that result in clinical candidates. These observations provide optimization guidelines for fragment-based drug discovery programs

Discovery of Subtype Selective Janus Kinase (JAK) Inhibitors by Structure-Based Virtual Screening

Janus kinase inhibitors represent a promising opportunity for the pharmaceutical intervention of various inflammatory and oncological indications. Subtype selective inhibition of these enzymes, however, is still a very challenging goal. In this study, a novel, customized virtual screening protocol was developed with the intention of providing an efficient tool for the discovery of subtype selective JAK2 inhibitors. The screening protocol involves protein ensemble-based docking calculations combined with an Interaction Fingerprint (IFP) based scoring scheme for estimating ligand affinities and selectivities, respectively. The methodology was validated in retrospective studies and was applied prospectively to screen a large database of commercially available compounds. Six compounds were identified and confirmed in vitro, with an indazole-based hit exhibiting promising selectivity for JAK2 vs JAK1. Having demonstrated that the described methodology is capable of identifying subtype selective chemical starting points with a favorable hit rate (11%), we believe that the presented screening concept can be useful for other kinase targets with challenging selectivity profiles

Comparative Evaluation of Covalent Docking Tools

Increased interest in covalent drug discovery led to the development of computer programs predicting binding mode and affinity of covalent inhibitors. Here we compare the performance of six covalent docking tools, AutoDock4, CovDock, FITTED, GOLD, ICM-Pro, and MOE, for reproducing experimental binding modes in an unprecedently large and diverse set of covalent complexes. It was found that 40–60% of the top scoring ligand poses are within 2.0 Å RMSD from the experimental binding mode. This rate showed program dependent increase and achieved 50–90% when the best RMSD among the top ten scoring poses was considered. This performance is comparable to that of noncovalent docking tools and therefore suggests that anchoring the ligand does not necessarily improve the accuracy of the prediction. The effect of various ligand and protein features on the docking performance was investigated. At the level of warhead chemistry, higher success rate was found for Michael additions, nucleophilic additions and nucleophilic substitutions than for ring opening reactions and disulfide formation. Increasing ligand size and flexibility generally affects pose predictions unfavorably, although AutoDock4, FITTED, and ICM-Pro were found to be less sensitive up to 35 heavy atoms. Increasing the accessibility of the target cysteine tends to result in improved binding mode predictions. Docking programs show protein dependent performance suggesting a target-dependent choice of the optimal docking tool. It was found that noncovalent docking into Cys/Ala mutated proteins by ICM-Pro and Glide reproduced experimental binding modes with only slightly lower performance and at a significantly lower computational expense than covalent docking did. Overall, our results highlight the key factors influencing the docking performance of the investigated tools and they give guidelines for selecting the optimal combination of warheads, ligands, and tools for the system investigated. Results also identify the most important aspects to be considered for developing improved protocols for docking and virtual screening of covalent ligands

Impact of Lipophilic Efficiency on Compound Quality

Lipophilic efficiency indices such as LLE and LELP were suggested to support balanced optimization of potency and ADMET profile. Here we investigated the performance of LLE and LELP on multiple data sets representing different stages of drug discovery including fragment and HTS hits and leads, development candidates, phase II compounds, and launched drugs. Analyzing their impact on ADME and safety properties and binding thermodynamics, we found that both LLE and LELP help identifying better quality compounds. LLE is sensible for the development stages but does not prefer fragment-type hits, while LELP has an advantage for this class of compounds and discriminates preferred starting points effectively. Both LLE and LELP have significant impact on ADME and safety profiles; however, LELP outperforms LLE in risk assessment at least on the present data set. On the basis of the results reported here, monitoring lipophilic efficiency metrics could contribute significantly to compound quality and might improve the output of medicinal chemistry programs

Multiple Fragment Docking and Linking in Primary and Secondary Pockets of Dopamine Receptors

A sequential docking methodology was applied to computationally predict starting points for fragment linking using the human dopamine D₃ receptor crystal structure and a human dopamine D₂ receptor homology model. Two focused fragment libraries were docked in the primary and secondary binding sites, and best fragment combinations were enumerated. Similar top scoring fragments were found for the primary site, while secondary site fragments were predicted to convey selectivity. Three linked compounds were synthesized that had 9-, 39-, and 55-fold selectivity in favor of D₃ and the subtype selectivity of the compounds was assessed on a structural basis

Structure-Based Consensus Scoring Scheme for Selecting Class A Aminergic GPCR Fragments

Aminergic G-protein coupled receptors (GPRCs) represent well-known targets of central nervous-system related diseases. In this study a structure-based consensus virtual screening scheme was developed for designing targeted fragment libraries against class A aminergic GPCRs. Nine representative aminergic GPCR structures were selected by first clustering available X-ray structures and then choosing the one in each cluster that performs best in self docking calculations. A consensus scoring protocol was developed using known promiscuous aminergic ligands and decoys as a training set. The consensus score (FrACS-fragment aminergic consensus score) calculated for the optimized protein ensemble showed improved enrichments in most cases as compared to stand-alone structures. Retrospective validation was carried out on public screening data for aminergic targets (5-HT1 serotonin receptor, TA(1) trace-amine receptor) showing 8-17-fold enrichments using an ensemble of aminergic receptor structures. The performance of the structure based FrACS in combination with our ligand-based prefilter (FrAGS) was investigated both in a retrospective validation on the ChEMBL database and in a prospective validation on an in-house fragment library. In prospective validation virtual fragment hits were tested on S-HT6 serotonin receptors not involved in the development of FrACS. Six out of the 36 experimentally tested fragments exhibited remarkable antagonist efficacies, and 4 showed IC50 values in the low micromolar or submicromolar range in a cell-based assay. Both retrospective and prospective validations revealed that the methodology is suitable for designing focused class A GPCR fragment libraries from large screening decks, commercial compound collections, or virtual databases

Molecular Dynamics Simulation at High Sodium Chloride Concentration: Toward the Inactive Conformation of the Human Adenosine A2A Receptor

The recently solved crystal structure of the human adenosine A2A receptor (hA2AR) shows the characteristics of a partially activated state. Experimental data suggests that high sodium chloride concentration shifts hA2AR to the inactive state. We found that molecular dynamics simulations at high sodium chloride concentration result in an inactive form of hA2AR reflected in the reformation of the “ionic lock” (Arg¹⁰²(3.50)−Glu²²⁸(6.30)) as well as in the reduction of the αC−αC distance between the intracellular sides of transmembrane helices 3 and 6 (TM3 and TM6). Interestingly, no such stabilization effect was observed at physiological concentrations. Our results suggest that the effect of high sodium chloride concentration might be exploited to generate an inactive state of hA2AR, which is more favorable for identifying pharmacologically relevant antagonists or inverse agonists

Fragment Based Optimization of Metabotropic Glutamate Receptor 2 (mGluR2) Positive Allosteric Modulators in the Absence of Structural Information

Metabotropic glutamate receptor 2 (mGluR2) positive allosteric modulators (PAMs) have been implicated as potential pharmacotherapy for psychiatric conditions. Screening our corporate compound deck, we identified a benzotriazole fragment (<b>4</b>) that was rapidly optimized to a potent and metabolically stable early lead (<b>16</b>). The highly lipophilic character of <b>16</b>, together with its limited solubility, permeability, and high protein binding, however, did not allow reaching of the proof of concept in vivo. Since further attempts on the optimization of druglike properties were unsuccessful, the original hit <b>4</b> has been revisited and was optimized following the principles of fragment based drug discovery (FBDD). Lacking structural information on the receptor–ligand complex, we implemented a group efficiency (GE) based strategy and identified a new fragment like lead (<b>60</b>) with more balanced profile. Significant improvement achieved on the druglike properties nominated the compound for in vivo proof of concept studies that revealed the chemotype being a promising PAM lead targeting mGluR2 receptors